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NMRG                                                      K. Pentikousis
Internet-Draft                                                      EICT
Intended status: Informational                              M. Sifalakis
Expires: November 5, 2015                            University of Basel
                                                                J. Nobre
                                 Federal University of Rio Grande do Sul
                                                             May 4, 2015


               Autonomic Networking Definitions Revisited
                     draft-pentikousis-nmrg-andr-02

Abstract

   This document revisits the autonomic networking terminology
   established in peer-reviewed literature, aiming to contribute to the
   ongoing discussion in the IRTF NMRG about how to move forward with
   standardizing various autonomic networking aspects.

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   the Trust Legal Provisions and are provided without warranty as
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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
     1.1.  Motivation  . . . . . . . . . . . . . . . . . . . . . . .   2
     1.2.  Scope . . . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Definitions . . . . . . . . . . . . . . . . . . . . . . . . .   3
   3.  Operational Considerations and Outlook  . . . . . . . . . . .   5
     3.1.  New Deployment Models . . . . . . . . . . . . . . . . . .   6
     3.2.  Programmable Network Elements and Functions . . . . . . .   6
     3.3.  Autonomic Planes  . . . . . . . . . . . . . . . . . . . .   6
     3.4.  DevOps  . . . . . . . . . . . . . . . . . . . . . . . . .   7
     3.5.  Autonomic Monitoring  . . . . . . . . . . . . . . . . . .   7
   4.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .   8
   5.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   9
   6.  Security Considerations . . . . . . . . . . . . . . . . . . .   9
   7.  Informative References  . . . . . . . . . . . . . . . . . . .   9
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  12

1.  Introduction

   The IRTF Network Management Research Group (NMRG) has been working on
   a set of definitions for autonomic networking.  Defining and agreeing
   on autonomic networking terminology is not an easy task as discussed
   in [TAN].  In general, autonomic operation is associated with a range
   of properties, such as self-configuration, self-healing, self-
   optimization, and self-protection [ACDawn].  It is worth pointing out
   that although there is some implicit consensus within the autonomic
   computing community on the key properties/attributes of an autonomic
   system, in the autonomic networking community this is not necessarily
   the case.  In the past, the common ground between different projects
   and different contexts of operation was the presence of self-*
   properties, without converging to a minimum set or different levels
   of autonomic behavior, or where this behavior needs to be manifested.

1.1.  Motivation

   Behringer et al.  [I-D.irtf-nmrg-autonomic-network-definitions]
   describe a set of design goals and non-goals for autonomic networking
   and introduce a model reference architecture in the context of future
   IETF standardization [I-D.behringer-autonomic-control-plane].

   Prior to this effort at NMRG, autonomic networking has been the focus
   of several research projects.  For example, Bouabene et al.  [ANA]
   detail an autonomic network architecture (ANA).  Nguengang et al.
   [UMFSpec] propose a unified management framework (UMF) which has



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   autonomic properties and functions at its core.  Chaparadza et al.
   [SelfFI] introduce an elegant and "standardizable" [sic] generic
   autonomic networking architecture (GANA) which they propose to be
   adopted as a reference model.  GANA was indeed further elaborated
   under the auspices of ETSI as a group specification [GANA].

   Jennings et al.  [TAM07] discuss the challenges one must deal with
   when applying autonomic principles to network management.  This
   includes translation from business rules to resources/services to be
   provided, contextual changes in the network, adaptation of the
   management control loops, and verification of dynamic models for
   machine learning purposes.  Samaan and Karmouch [SK09] analyze the
   requirements and the main contributions for the building blocks of
   autonomic network management systems, describe a classification
   methodology which compares previously proposed architectures, suggest
   a reference framework, and point to a set of research challenges.

   This list of earlier work in only indicative of the breadth of
   research in this area over the last decade.  However, standardization
   remains an open question and deployment has been limited to specific
   mechanisms only [I-D.irtf-nmrg-an-gap-analysis].

1.2.  Scope

   We concur with Behringer et al.
   [I-D.irtf-nmrg-autonomic-network-definitions] that for most of the
   work in IETF it suffices to define autonomic behaviour at the node
   level.  However, recent standardization efforts in the IETF, such as,
   for example, I2RS [I-D.ietf-i2rs-problem-statement], SFC [RFC7498],
   ABNO [RFC7491], SUPA [I-D.pentikousis-supa-mapping], and LIME to name
   a few, and new IRTF research groups such as SDNRG and NFVRG, indicate
   that NMRG should perhaps dig a bit deeper into the definitions for
   autonomic networking that will be of tangible benefit to the
   researcher and practitioner communities alike.  In particular, one
   could reconsider the aspects of defining node-level autonomicity
   only.

   This document revisits the autonomic networking definitions proposed
   earlier in the peer-reviewed literature Section 2, and relates them
   with recent developments aiming to assist in the definition of a
   coherent terminology in this emerging area of standardization at the
   IETF.

2.  Definitions

   After some thorough analysis and discussion, Schmid et al.  [TAN] put
   forward the following definition, which captures in a concrete and
   concise manner the essence of autonomicity:



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      An Autonomic System is a system that operates and serves its
      purpose by managing its own self without external intervention
      even in case of environmental changes.

   Note that the authors explicitly define autonomicity at the system
   level, not at the node level.  They go on to list the minimum set of
   properties that an autonomic system should possess.  Namely, an
   autonomic system is

   o  automatic, i.e. it can "self-control its internal functions and
      operations"

   o  adaptive, i.e. it can change its "configuration, state and
      functions", and

   o  aware, i.e. it can "monitor its operational context".

   In principle, an autonomic system could wholly replaces a non-
   autonomic one.  In practice, however, real-world deployments will
   include legacy network elements and services as well as new autonomic
   ones.

   A salient paper in the autonomic networking area is [FOCALE], in
   which Strassner et al. lay the foundation for an autonomic network
   architecture.  We will not delve into the details of FOCALE, but we
   do note that the authors define three types of managed components
   according to their autonomic capabilities.  In the remainder of this
   document we consider that FOCALE "components" equate to network
   resources as defined in [RFC7426], i.e. each network resource is a
   "physical or virtual component available within a system", and expand
   these definitions further.

   In this sense, legacy equipment can be seen as autonomically unaware
   resources, and can only be managed using traditional mechanisms.  In
   practice, field equipment could be upgraded to support certain
   autonomic features, thus becoming autonomically-aware managed network
   resources.  This type of network element would typically require a
   mediation layer as suggested in [FOCALE] or at the very least certain
   system software updates.  Finally, a deployment could include fully
   autonomically-enabled network resources.  FOCALE explicitly aims to
   "accommodate legacy components" and foresees the deployment of an
   autonomic manager "that orchestrates the behaviour of other autonomic
   components in the system."

   Figure 1 illustrates a simple sketch of an autonomic networking
   control loop, based on Fig. 2 of [FOCALE].  In short, an autonomic
   manager gathers data from the managed resource(s), evaluates the
   current state, compares it with the desired one, and configures the



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   managed resource as necessary.  As illustrated, this simple system
   possess the minimum set of properties introduced above.


                            +---------------------+
    (Maintenance Loop)      | Actual vs. desired  |  Autonomic manager
            +-------------->| state evaluation    |
            |               | and decision making |
            |               +---------o-----------+
            v                         |
    +----------------+                | New configuration
    | Data gathering |                | (Adjustment Loop)
    +----------------+                |
            ^                         v
            |                +------------------+
            +----------------o Managed resource |
                             +------------------+

      Figure 1: Simple sketch of an autonomic networking control loop

   All three types of network resources (i.e. autonomically-unaware,
   autonomically-aware, and autonomically-enabled) need to be managed.
   One viable approach is proposed by Nguengang et al.  [UMFSpec] who
   describe an architecture based on the definition of two types of
   management systems depending on the capacity of the underlying nodes,
   namely an Enhanced Legacy Management System (ELMS) or a future
   management system.  In this context, it is worth considering the
   approaches taken in other disciplines.  For example, in aviation,
   auto-navigation systems solve this challenge by means of distributed
   consensus among an odd-number of controllers/managers that
   independently carry out the tasks of data gathering and state
   evaluation, and then make an election for each decision.  On the
   other hand, biologically-inspired systems have emergent coordination
   (of managers) following principles such as entropy or mass action.

   Finally, autonomic properties are highly desirable in the context of
   new mobile architectures.  For example, Barth and Kuehn [SON4G]
   discuss the need for self-* properties in the context of small cell
   deployments in 3GPP 4G/LTE, while Hamalainen et al.  [LTESON] provide
   a comprehensive guide and handy references to the efforts in 3GPP
   along these lines.

3.  Operational Considerations and Outlook

   This section briefly describes emerging operational considerations
   that in the authors' view should be taken into account as we move
   forward with autonomic networking standardization in the IETF and
   IRTF context.



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3.1.  New Deployment Models

   Strassner et al.  [FOCALE] highlight that an important goal of
   autonomics is "making the life of the user easier by changing the
   focus from a computer-centric to a task-centric model".  Deployment
   of new network technologies is typically a time-consuming, labour-
   intensive and cumbersome task.  In the past, we have seen that if the
   newly designed infrastructure cannot be managed satisfactorily,
   adverse results such as service launch delays may be inevitable.  As
   we move forward with new deployment models which are oriented towards
   softwarized and cloudified network functions, autonomic networking
   principles may prove invaluable.

   As per [TAN], autonomic systems are by design programmable, which
   bodes well with the emerging deployment models which emphasize
   agility and shorter technology introduction cycles.  We argue that
   autonomic networking definitions, goals and gap analysis within the
   context of IETF standardization should take this more into
   consideration.  Further, recent initiatives such as SUPA
   [I-D.pentikousis-supa-mapping] point towards infrastructures which
   are managed through intent (generic policies), for instance, as
   opposed to network element specific configuration.

3.2.  Programmable Network Elements and Functions

   Although the development of models such as FoRCES [RFC5812] coincided
   with the core of the above-mentioned autonomic networking research
   literature, by and large, the two areas did not cross-pollinate.  It
   appears that as SDN and NFV principles reach a wider audience of
   researchers and practitioners, fully programmable network elements
   and functions could be further introduced in autonomic networking
   architectures.  Indeed, moving towards a "task-centric model" relates
   well with other efforts in IETF such as SFC [RFC7498]

3.3.  Autonomic Planes

   Recent work at the SDNRG [RFC7426] highlighted the need for the wider
   SDN community to think in terms of control, management, and
   operational planes comprehensiveness and complementarity.  As we have
   seen above, earlier work in autonomic networking has been primarily
   focusing on management aspects (cf.  [UMFSpec]), while recent work in
   NMRG is focusing on standardizing an autonomic networking control
   plane [I-D.behringer-autonomic-control-plane].

   For an autonomic plane, there is the challenge on "which
   functionality to place where".  For example, one could consider an
   architecture in which all three planes have autonomic features.
   Alternatively, one could adopt a knowledge plane approach [KP2003]



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   establishing a separate, virtual/logical plane.  A way forward could
   be to consider autonomics in NMRG in the context of programmable
   networks and through a more comprehensive manner.

3.4.  DevOps

   John et al.  [NSC] elaborate on the concept of continuous network
   service delivery.  In this context, the authors argue for the need of
   programmable observation points which could be inserted in a dynamic
   service chain on demand.  They expect that future service provider
   DevOps would require new management technologies "based on the
   experience from data centers" thus "addressing the challenges of
   dynamic service chaining".  This bodes well with the model
   illustrated in Figure 1 and we could expect more results in this
   direction in the future.

3.5.  Autonomic Monitoring

   Network monitoring is related to the mechanisms employed to perform
   measurements and collect the respective results.  These mechanisms
   are some of the most important tools employed by network
   administrators.  Monitoring results encompass metrics such as delay
   (one-way or round-trip), jitter, throughput, packet loss, protocol/
   application usage, among others.  Results can be used in different
   contexts, such as pre-deployment validation and measurement of in-
   band live network performance characteristics, and by several
   applications, such as intrusion detection and lawful interception.

   Traditional (i.e., non-autonomic) monitoring mechanisms usually rely
   on the predetermined production of measurements results.  Thus, such
   mechanisms are not able to dynamically adapt to different operational
   conditions during runtime.  On the other hand, autonomic monitoring
   mechanisms are able to adjust themselves in order to optimize their
   operation.  This can be done using several techniques, such as
   reinforcement learning and neural networks.

   Several classifications have been proposed regarding autonomic
   monitoring.  Samaan and Karmouch [SK09] discuss a classification
   methodology for autonomic monitoring methods in the context of an
   analysis of current and future research directions of autonomic
   network management.  The authors provide a classification of
   autonomic monitoring approaches considering the following classes:
   active versus passive monitoring and distributed versus centralized
   monitoring.  The authors also comment on monitoring granularity
   (measurements can be performed at the byte-, packet-, flow- or
   aggregated-traffic levels); monitoring timing (fixed time, event-
   based, and on-demand); and monitoring programmability (levels on what




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   the monitoring mechanism itself can dynamically modify with respect
   to its operation).

   In the following we provide a set of literature pointers to
   monitoring systems which exhibit autonomic features.  Note that such
   mechanisms exhibit different levels of autonomic monitoring
   functionality and employ different techniques to support this
   functionality.

   Massie et al.  [MCC04] proposed Ganglia, a scalable, distributed
   system monitor tool for high-performance computing systems such as
   clusters and grids.  This system is based on a hierarchical design
   targeted at federations of clusters and it relies on a multicast-
   based listen/announce protocol to monitor state within network nodes.
   Using a set of programmable interfaces, Ganglia follows a passive
   distributed monitoring approach where monitoring programmability is
   left to the applications.

   Song et al.  [SQZ06] proposed NetQuest, a centralized monitoring
   control of active measurement mechanisms with self-programmability
   features.  NetQuest models the selection of monitoring
   functionalities and uses Bayesian experimental design concepts to
   define the solution parameters.

   Duarte et al.  [DNGT11] proposed ManP2P-ng, a system focused in
   materializing distributed self-healing features through the use of
   P2P management overlays and high-level descriptions called workplans.
   Workplans are used to set up the self-healing parameters regarding
   managed devices and management peers.  The self-healing service is
   composed of independent monitoring and healing services.

   Sekar et al.  [SRWZKA08] proposed CSAMP, a centralized optimization
   engine for system-wide flow monitoring.  The main features of CSAMP
   are the use of traffic information to steer flow sampling and hash-
   based packet selection through a centralized engine for the
   distribution of measurement responsibilities across routers.

   Pietro et al.  [PHCN10] proposed DECON, a decentralized coordination
   system aimed at assigning passive monitoring probes.  DECON uses
   traffic information from probes seeing a particular ow to decide
   which one shoud do the actual monitoring.  After that, messages are
   sent back to probes communicating the decision.

4.  Acknowledgements

   This document would not have been possible without the stimulating
   discussion during the NMRG meeting at IETF 90 in Toronto.  Many
   thanks to all participants.



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5.  IANA Considerations

   This memo includes no request to IANA.

6.  Security Considerations

   This document does not propose a new network architecture or protocol
   and as such does not have any impact on the security of the Internet.

   Autonomic networking introduces a range of opportunities for formal
   verification techniques which could increase trustworthiness,
   although this is clearly beyond the scope of this first version of
   this document.  Interested readers should consult [ACSec] for an
   early exploration of the issues at hand in the context of autonomic
   computing.

7.  Informative References

   [ACDawn]   Ganek, A. G., and T. A. Corbi, "The dawning of the
              autonomic computing era", IBM systems Journal, 42(1), 5-18
              , 2003.

   [ACSec]    Chess, D. M., Palmer, C. C., and S. R. White, "Security in
              an autonomic computing environment", IBM systems Journal,
              42(1), 107-118 , 2003.

   [ANA]      Bouabene, G., Jelger, C., Tschudin, C., Schmid, S.,
              Keller, A., and M. May, "The autonomic network
              architecture (ANA)", Journal on Selected Areas in
              Communications, 28(1), 4-14 IEEE, 2003.

   [DNGT11]   Duarte, P. A. P. R., Nobre, J. C., Granville, L. Z.,
              Tarouco, L. M. R., "A P2P-Based Self-Healing Service for
              Network Maintenance", Proceedings of the 12th IFIP/IEEE
              International Symposium on Integrated Network Management
              (IM) IEEE, 2011.

   [FOCALE]   Strassner, J., Agoulmine, N., and E. Lehtihet, "FOCALE: A
              novel autonomic networking architecture", Proc. Latin
              American Autonomic Computing Symposium (LAACS), Campo
              Grande, Brazil, July 2006.

   [GANA]     ETSI GS AFI 002, , "Autonomic network engineering for the
              self-managing Future Internet (AFI): GANA Architectural
              Reference Model for Autonomic Networking, Cognitive
              Networking and Self-Management.", April 2013.





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   [I-D.behringer-autonomic-control-plane]
              Behringer, M., Bjarnason, S., BL, B., and T. Eckert, "An
              Autonomic Control Plane", draft-behringer-autonomic-
              control-plane-00 (work in progress), June 2014.

   [I-D.ietf-i2rs-problem-statement]
              Atlas, A., Nadeau, T., and D. Ward, "Interface to the
              Routing System Problem Statement", draft-ietf-i2rs-
              problem-statement-06 (work in progress), January 2015.

   [I-D.irtf-nmrg-an-gap-analysis]
              Jiang, S., Carpenter, B., and M. Behringer, "General Gap
              Analysis for Autonomic Networking", draft-irtf-nmrg-an-
              gap-analysis-05 (work in progress), March 2015.

   [I-D.irtf-nmrg-autonomic-network-definitions]
              Behringer, M., Pritikin, M., Bjarnason, S., Clemm, A.,
              Carpenter, B., Jiang, S., and L. Ciavaglia, "Autonomic
              Networking - Definitions and Design Goals", draft-irtf-
              nmrg-autonomic-network-definitions-07 (work in progress),
              March 2015.

   [I-D.pentikousis-supa-mapping]
              Pentikousis, K. and D. Zhang, "Simplified Use of Policy
              Abstractions (SUPA): Configuration and Policy Mapping",
              draft-pentikousis-supa-mapping-04 (work in progress),
              March 2015.

   [KP2003]   Clark, D. D., Partridge, C. , et al., "A Knowledge Plane
              for the Internet", Proc. SIGCOMM, Karlsruhe, Germany ACM,
              August 2003.

   [LTESON]   Hamalainen, S., Sanneck, H., and C. Sartori, "LTE Self-
              Organising Networks (SON): Network Management Automation
              for Operational Efficiency", John Wiley & Sons , 2012.

   [MCC04]    Massie, M.L. and Chun, B.N. and Culler, D.E., "The ganglia
              distributed monitoring system: design, implementation, and
              experience", Parallel Computing, vol. 30, no. 7, pp.
              817-840 Elsevier, 2004.

   [NSC]      John, W., Pentikousis, K., et al., "Research directions in
              network service chaining", Proc. SDN for Future Networks
              and Services (SDN4FNS), Trento, Italy IEEE, November 2013.







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   [PHCN10]   di Pietro, A. and Huici, F. and Costantini, D. and
              Niccolini, S., "DECON: Decentralized Coordination for
              Large-Scale Flow Monitoring", Proceedings of the IEEE
              Conference on Computer Communications (INFOCOM) Workshops
              IEEE, 2010.

   [RFC5812]  Halpern, J. and J. Hadi Salim, "Forwarding and Control
              Element Separation (ForCES) Forwarding Element Model", RFC
              5812, March 2010.

   [RFC7426]  Haleplidis, E., Pentikousis, K., Denazis, S., Hadi Salim,
              J., Meyer, D., and O. Koufopavlou, "Software-Defined
              Networking (SDN): Layers and Architecture Terminology",
              RFC 7426, January 2015.

   [RFC7491]  King, D. and A. Farrel, "A PCE-Based Architecture for
              Application-Based Network Operations", RFC 7491, March
              2015.

   [RFC7498]  Quinn, P. and T. Nadeau, "Problem Statement for Service
              Function Chaining", RFC 7498, April 2015.

   [SK09]     Samaan, N. and A. Karmouch, "Towards Autonomic Network
              Management: an Analysis of Current and Future Research
              Directions", Communications Surveys & Tutorials, vol. 11,
              no. 3, pp. 22-36 IEEE, 2009.

   [SON4G]    Barth, U., and E. Kuehn, "Self-organization in 4G mobile
              networks: Motivation and vision", Proc. 7th International
              Symposium on Wireless Communication Systems (ISWCS), York,
              UK, pp. 731-735, IEEE, September 2010.

   [SQZ06]    Song, H. H., Qiu, L., Zhang, Y., "NetQuest: a flexible
              framework for large-scale network measurement", ACM
              SIGMETRICS Performance Evaluation Review, Vol. 34. No. 1.
              ACM, 2006.

   [SRWZKA08]
              Sekar, V. and Reiter, M.K. and Willinger, W. and Zhang, H.
              and Kompella, R.R. and Andersen, D. G., "CSAMP: a system
              for network-wide flow monitoring", Proceedings of the 5th
              USENIX Symposium on Networked Systems Design and
              Implementation (NSDI) USENIX, 2008.








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   [SelfFI]   Chaparadza, R., Papavassiliou, S., et al., "Creating a
              viable Evolution Path towards Self-Managing Future
              Internet via a Standardizable Reference Model for
              Autonomic Network Engineering", Future Internet Assembly
              (pp. 136-147) IOS Press, 2009.

   [TAM07]    Jennings, B., van der Meer, s. et al., "Towards autonomic
              management of communications networks", Communications
              Magazine, vol. 45, no. 10, pp. 112-121 IEEE, 2007.

   [TAN]      Schmid, S., Sifalakis, M., and D. Hutchison, "Towards
              autonomic networks", Proc. Autonomic Networking, LNCS
              4195, pp. 1-11 Springer, 2006.

   [UMFSpec]  Nguengang, G. (ed.), et al., "UMF Specifications, Release
              1", FP7-UNIVERSELF-Deliverable D2.1 , July 2011.

Authors' Addresses

   Kostas Pentikousis
   EICT GmbH
   EUREF-Campus Haus 13
   Torgauer Strasse 12-15
   10829 Berlin
   Germany

   Email: k.pentikousis@eict.de


   Manolis Sifalakis
   University of Basel
   Bernoullistrasse 16
   4056 Basel
   Switzerland

   Email: sifalakis.manos@unibas.ch


   Jeferson Campos Nobre
   Federal University of Rio Grande do Sul
   Institute of Informatics
   Porto Alegre
   Brazil

   Email: jcnobre@inf.ufrgs.br






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